In multilabel classification each example is represented with features and associated with multiple labels. Multilabel classification aims to predict set of labels for unseen instances. Researchers have developed multilabel classification using both the problem transformation approach and algorithm adaptation approach. An algorithm called ML-kNN that follows algorithm adaptation approach has been developed and being used to perform multilabel classification. However it does not considers label correlation and thus results in lesser prediction accuracy. A new approach called CML-kNN reported in the literature exploits label correlation using both Intra-Coupling and Inter-Coupling label similarities between the labels to provide better accuracy than that of ML-kNN, but curse of dimensionality is the great challenges in multilabel data. So to address this problem a new approach called CML-kNN with feature selection is presented in this work. The basic idea of this work is to investigate the performance of CML-kNN with and without feature selection. The experiments indicate that proposed CML-kNN with feature selection method achieves superior performance than existing CML-kNN method.

1. Gjorgji Madjarov ,DragiKocev , Dejan Gjorgjevikj , Saso Dzeroski, "An extensive experimental comparison of methods for multi-label learning", Pattern Recognition 45 (2012) 3084-3104
2. Tsoumakas, G. , Katakis, I. "Multi-label classification: An overview" International Journal of Data Warehousing and Mining (IJDWM) 3(3), 1-13 (2007)
3. Boutell, M. R. , Luo, J. , Shen, X. , Brown, C. M. "Learning multi-label scene classification", Pattern recognition 37(9), 1757-1771 (2004)
4. Grigorios Tsoumakas, Ioannis Katakis, and Ioannis Vlahavas, "Mining Multi-label Data", Data Mining and Knowledge Discovery Handbook, Springer, Berlin Heidelberg, 2010, pp. 667-685.
5. Read, J. , Pfahringer, B. , Holmes, G. , Frank, E. : Classifier chains for multi-labelclassification. Machine Learning 85(3), 333–359 (2011).
6. Zhang, M. L. , Zhou, Z. H. , "ML-kNN: A lazy learning approach to multi-label learning", Pattern recognition 40(7), 2038-2048 (2007) pp. 401-406. Springer (2008)
7. Spyromitros, E. , Tsoumakas, G. , Vlahavas, I. , "An empirical study of lazy multilabel classification algorithms", Artificial Intelligence: Theories, Models and Applications, pp. 401-406. Springer (2008)
8. Cheng, W. , HÂlullermeier, E. , "Combining instance-based learning and logistic regression for multi-label classification",Machine Learning 76(2-3), 211-225 (2009)
9. Boriah, S. , Chandola, V. , Kumar, V. , "Similarity measures for categorical data: A comparative evaluation", red 30(2), 3 (2008)
10. Cao, L. , "Coupling learning of complex interactions",Information Processing and Management (2014)
11. Cao, L. , Ou, Y. , Yu, P. S. , "Coupled behavior analysis with applications", Knowledge and Data Engineering, IEEE Transactions on 24(8), 1378-1392 (2012)
12. Liu, C. , Cao, L. , Yu, P. S. , "Coupled fuzzy k-nearest neighbors classification of imbalanced non-iid categorical data", Neural Networks (IJCNN), 2014 International Joint Conference on. pp. 1122-1129. IEEE (2014)
13. Chunming Liu and Longbing Cao, "A Coupled k-Nearest Neighbor Algorithm for Multi-label Classification", Springer PAKDD 2015, Part I, LNAI 9077, pp. 176- 187, 2015
14. Shining Li, Zhenhai Zhang, Jiaqi Duan "An Ensemble Multi-Label Feature Selection Algorithm Based on Information Entropy" The International Arab Journal of Information Technology, Vol. 11, No. 4, July 2014.
15. Prajakta Chaudhari , Prof. Dr. S. S. Sane, " Review on Multilabel Classification Algorithms ",IJSRD – International Journal for Scientific Research and Development Vol. 3, Issue 11, 2016 ISSN (Online) 2321-0613

Algorithm Adaption K Nearest Neighbor Label Correlation Ml-knn Multilabel Classification